| 57891. |
- Rahiminejad, Sofia, 1987, et al.
(författare)
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Micromachined gap waveguide devices for above 100 GHz
- 2014
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Ingår i: Swedish Microwave Days March 11-12, 2014.
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Konferensbidrag (refereegranskat)abstract
- Gap waveguide technology is a fundamentally new highfrequencywaveguide technology [1]. It does not need anyelectrical contact between the split blocks which gives it anadvantage, compared to the rectangular waveguide.Rectangular waveguides are often fabricated by milling.However there are issues with milling when constructingwaveguides above 100 GHz. MEMS technology can offerhigh-precision fabrication and thus enables the path for newtypes of high-frequency components. An overview of themicromachined gap waveguide devices that has beenfabricated for above 100 GHz are presented here.
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| 57892. |
- Rahiminejad, Sofia, 1987, et al.
(författare)
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Micromachined gap waveguides for 100 GHz applications
- 2013
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Ingår i: 2013 7th European Conference on Antennas and Propagation, EuCAP 2013. - : EurAAP. - 9788890701832 ; , s. 1935-1938
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Konferensbidrag (refereegranskat)abstract
- The present paper demonstrates groove gap waveguides at around 100 GHz, fabricated on Gold-plated micromachined silicon. Three different groove gap waveguides have been manufactured and measured: a resonator for determining Q-factor and thereby attenuation, a straight waveguide, and a waveguide with two 90 degree bends.
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| 57893. |
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| 57894. |
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| 57895. |
- Rahiminejad, Sofia, et al.
(författare)
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SU8 ridge-gap waveguide resonator
- 2014
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Ingår i: International journal of microwave and wireless technologies. - 1759-0795 .- 1759-0787. ; 6:5, s. 459-465
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present the first ridge-gap waveguide resonator made with a polymer base. It is designed for the frequency range 220-325 GHz, and is fabricated solely using a Au coated two-layer SU8-based process. The design is based on previous work done with Si. The new process has advantages such as fewer and cheaper process steps. The SU8 ridge-gap waveguide resonator is made in order to obtain attenuation characteristics via the measured Q-factor of the resonator. The ridge-gap waveguide resonator has the same dimensions as the previous one fabricated in Si, and the same thickness of the Au coating. The SU8-based resonator shows an attenuation loss of 0.41 dB/mm at 282.2 GHz compared to the Si-based resonator with an attenuation loss of 0.043 dB/mm at 283.5 GHz. This makes the SU8 process a more cost-effective alternative to the Si process
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| 57896. |
- Rahman, Aniq Ur, et al.
(författare)
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Network Graph Generation through Adaptive Clustering and Infection Dynamics: A Step Towards Global Connectivity
- 2022
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Ingår i: IEEE Communications Letters. - 1558-2558 .- 1089-7798. ; 26:4, s. 783-787
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Tidskriftsartikel (refereegranskat)abstract
- More than 40% of the world’s population is not connected to the internet, majorly due to the lack of adequate infrastructure. Our work aims to bridge this digital divide by proposing solutions for network deployment in remote areas. Specifically, a number of access points (APs) are deployed as an interface between the users and backhaul nodes (BNs). The main challenges include designing the number and location of the APs, and connecting them to the BNs. In order to address these challenges, we first propose a metric called connectivity ratio to assess the quality of the deployment. Next, we propose an agile search algorithm to determine the number of APs that maximizes this metric and perform clustering to find the optimal locations of the APs. Furthermore, we propose a novel algorithm inspired by infection dynamics to connect all the deployed APs to the existing BNs economically. To support the existing terrestrial BNs, we investigate the deployment of non-terrestrial BNs, which further improves the network performance in terms of average hop count, traffic distribution, and backhaul length. Finally, we use real datasets from a remote village to test our solution.
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| 57897. |
- Rahman, Hamidur, et al.
(författare)
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Deep Learning based Person Identification using Facial Images
- 2018
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Ingår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225. - Cham : Springer International Publishing. - 9783319762128 ; , s. 111-115
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Konferensbidrag (refereegranskat)abstract
- Person identification is an important task for many applications for example in security. A person can be identified using finger print, vocal sound, facial image or even by DNA test. However, Person identification using facial images is one of the most popular technique which is non-contact and easy to implement and a research hotspot in the field of pattern recognition and machine vision. n this paper, a deep learning based Person identification system is proposed using facial images which shows higher accuracy than another traditional machine learning, i.e. Support Vector Machine.
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| 57898. |
- Rahman, Hasibur, 1985-
(författare)
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Distributed Intelligence-Assisted Autonomic Context-Information Management : A context-based approach to handling vast amounts of heterogeneous IoT data
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As an implication of rapid growth in Internet-of-Things (IoT) data, current focus has shifted towards utilizing and analysing the data in order to make sense of the data. The aim of which is to make instantaneous, automated, and informed decisions that will drive the future IoT. This corresponds to extracting and applying knowledge from IoT data which brings both a substantial challenge and high value. Context plays an important role in reaping value from data, and is capable of countering the IoT data challenges. The management of heterogeneous contextualized data is infeasible and insufficient with the existing solutions which mandates new solutions. Research until now has mostly concentrated on providing cloud-based IoT solutions; among other issues, this promotes real-time and faster decision-making issues. In view of this, this dissertation undertakes a study of a context-based approach entitled Distributed intelligence-assisted Autonomic Context Information Management (DACIM), the purpose of which is to efficiently (i) utilize and (ii) analyse IoT data.To address the challenges and solutions with respect to enabling DACIM, the dissertation starts with proposing a logical-clustering approach for proper IoT data utilization. The environment that the number of Things immerse changes rapidly and becomes dynamic. To this end, self-organization has been supported by proposing self-* algorithms that resulted in 10 organized Things per second and high accuracy rate for Things joining. IoT contextualized data further requires scalable dissemination which has been addressed by a Publish/Subscribe model, and it has been shown that high publication rate and faster subscription matching are realisable. The dissertation ends with the proposal of a new approach which assists distribution of intelligence with regard to analysing context information to alleviate intelligence of things. The approach allows to bring few of the application of knowledge from the cloud to the edge; where edge based solution has been facilitated with intelligence that enables faster responses and reduced dependency on the rules by leveraging artificial intelligence techniques. To infer knowledge for different IoT applications closer to the Things, a multi-modal reasoner has been proposed which demonstrates faster response. The evaluations of the designed and developed DACIM gives promising results, which are distributed over seven publications; from this, it can be concluded that it is feasible to realize a distributed intelligence-assisted context-based approach that contribute towards autonomic context information management in the ever-expanding IoT realm.
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| 57899. |
- Rahman, Hamidur, et al.
(författare)
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Ins and Outs of Big Data : A Review
- 2016
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Konferensbidrag (refereegranskat)abstract
- Today with the fast development of digital technologies and advance communications a gigantic amount of data sets with massive and complex structures called ‘Big data’ is being produced everyday enormously and exponentially. Again, the arrival of social media, advent of smart homes, offices and hospitals are connected as Internet of Things (IoT), this influence also a lot to Big data. According to the study, Big data presents data sets with large magnitude including structured, semi-structured or unstructured data. The study also presents the new technologies for data analyzing, collecting, fast searching, proper sharing, exact storing, speedy transferring, hidden pattern visualization and violations of privacy etc. This paper presents an overview of ins and outs of Big Data where the content, scope, samples, methods, advantages, challenges and privacy of Big data have been discussed. The goal of this article is to provide big data knowledge to the research community for the sake of its many real life applications such as traffic management, driver monitoring, health care in hospitals, meteorology and so on.
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| 57900. |
- Rahman, Hamidur, et al.
(författare)
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Non-contact-based driver's cognitive load classification using physiological and vehicular parameters
- 2020
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Ingår i: Biomedical Signal Processing and Control. - : ELSEVIER SCI LTD. - 1746-8094 .- 1746-8108. ; 55
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Tidskriftsartikel (refereegranskat)abstract
- Classification of cognitive load for vehicular drivers is a complex task due to underlying challenges of the dynamic driving environment. Many previous works have shown that physiological sensor signals or vehicular data could be a reliable source to quantify cognitive load. However, in driving situations, one of the biggest challenges is to use a sensor source that can provide accurate information without interrupting diverging tasks. In this paper, instead of traditional wire-based sensors, non-contact camera and vehicle data are used that have no physical contact with the driver and do not interrupt driving. Here, four machine learning algorithms, logistic regression (LR), support vector machine (SVM), linear discriminant analysis (LDA) and neural networks (NN), are investigated to classify the cognitive load using the collected data from a driving simulator study. In this paper, physiological parameters are extracted from facial video images, and vehicular parameters are collected from controller area networks (CAN). The data collection was performed in close collaboration with industrial partners in two separate studies, in which study-1 was designed with a 1-back task and study-2 was designed with both 1-back and 2-back task. The goal of the experiment is to investigate how accurately the machine learning algorithms can classify drivers' cognitive load based on the extracted features in complex dynamic driving environments. According to the results, for the physiological parameters extracted from the facial videos, the LR model with logistic function outperforms the other three classification methods. Here, in study-1, the achieved average accuracy for the LR classifier is 94% and in study-2 the average accuracy is 82%. In addition, the classification accuracy for the collected physiological parameters was compared with reference wire-sensor signals. It is observed that the classification accuracies between the sensor and the camera are very similar; however, better accuracy is achieved with the camera data due to having lower artefacts than the sensor data.
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